In this paper, we developed BreastScreening-AI within two scenarios for the classification of
multimodal beast images:(1) Clinician-Only; and (2) Clinician-AI. The novelty relies on the …

Semantic segmentation is one of the most challenging tasks in computer vision. However, in
many applications, a frequent obstacle is the lack of labeled images, due to the high cost of …

Similar to other multi-modal pixel-level prediction tasks, existing RGB-T semantic
segmentation methods usually employ a two-stream structure to extract RGB and thermal …

Excellent performance has been achieved on semi-supervised medical image
segmentation, but existing algorithms perform relatively poorly for objects with variable …

Artificial intelligence decision systems play an important supporting role in the field of
medical information. Medical image analysis is an important part of decision systems and an …

Existing neural network segmentation schemes perform well in the task of segmenting
images of organs with large areas and clear morphology, such as the liver and lungs …

Filters and wrappers represent two mainstream approaches to feature selection (FS).
Although evolutionary wrapper-based FS outperforms filters in addressing real-world …

The scarcity of annotations poses a significant challenge in medical image analysis. Large-
scale pre-training has emerged as a promising label-efficient solution, owing to the …

This study investigates the effects of including patients' clinical information on the
performance of deep learning (DL) classifiers for disease location in chest X-ray images …

Deep learning technologies have been widely adopted in medical imaging due to their
ability to extract features from images and make accurate diagnoses automatically. Medical …